Reducing Temporal Redundancy for Efficient Vision-Language-Action Inference

• URL: http://arxiv.org/abs/2607.12287v1
• Date: Tue, 14 Jul 2026 02:48:31 GMT
• Title: Reducing Temporal Redundancy for Efficient Vision-Language-Action Inference
• Abstract summary: Vision-Language-Action (VLA) models exhibit strong generalization for robotic manipulation, yet their high inference latency limits real time deployment.<n>We propose a system level acceleration strategy that reduces computation in both perception and action generation.<n> Experiments on Libero, RobotWin, and Real Robot Platforms demonstrate over 2 times speedup while maintaining high performance.
• Score: 19.683948028719808
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Vision-Language-Action (VLA) models exhibit strong generalization for robotic manipulation, yet their high inference latency limits real time deployment. We identify two primary sources of temporal redundancy in existing VLA pipelines: repeated visual encoding of highly similar consecutive frames and multi step iterative sampling in diffusion based policies. To address this, we propose a system level acceleration strategy that reduces computation in both perception and action generation. On the perception side, we incrementally update only tokens corresponding to dynamic scene regions instead of re-encoding entire frames. On the policy side, we compress diffusion sampling into a compact 2-step schedule through efficiency oriented training while preserving action precision. Experiments on Libero, RobotWin, and Real Robot Platforms demonstrate over 2 times speedup while maintaining high performance, achieving up to 98% success rate on general manipulation benchmarks. Our codes will be released on Github.

Related papers

• StreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models [84.17128030384099]
  StreamPI is a streaming multimodal temporal modeling framework.<n>It equips single-frame VLA with temporal reasoning capability without introducing any additional parameters.<n> Experiments on real-robot tasks spanning memory-dependent and precise perception scenarios, as well as the simulation benchmark LIBERO, demonstrate that StreamPI outperforms pi0.5 across diverse tasks.
  arXiv  Detail & Related papers  (2026-08-26T17:33:19Z)
• DepthCache: Depth-Guided Training-Free Visual Token Merging for Vision-Language-Action Model Inference [5.305950698447464]
  Vision-Language-Action (VLA) models enable generalist robotic manipulation but suffer from high inference latency.<n>We present DepthCache, a training-free framework that leverages depth as a structural prior for visual token compression.<n>On the LIBERO benchmark, DepthCache achieves up to 1.28x inference speedup with less than 1% average success rate degradation.
  arXiv  Detail & Related papers  (2026-03-11T06:40:44Z)
• StreamVLA: Breaking the Reason-Act Cycle via Completion-State Gating [8.031311182554981]
  Long-horizon robotic manipulation requires bridging the gap between high-level planning and low-level control.<n>Current Vision-Language-Action models often entangle these processes, performing redundant multimodal reasoning at every timestep.<n>We present StreamVLA, a dual-system architecture that unifies textual task decomposition, visual goal imagination, and continuous action generation within a single parameter-efficient backbone.
  arXiv  Detail & Related papers  (2026-02-01T08:51:17Z)
• Asynchronous Fast-Slow Vision-Language-Action Policies for Whole-Body Robotic Manipulation [10.09057399213028]
  Vision-Language-Action (VLA) systems integrate a Vision-Language Model (VLM) for semantic reasoning with an action expert generating continuous action signals.<n>We introduce a truly asynchronous Fast-Slow VLA framework (DuoCore-FS) to organize the system into a fast pathway for action generation and a slow pathway for rich VLM reasoning.
  arXiv  Detail & Related papers  (2025-12-23T09:28:20Z)
• dVLA: Diffusion Vision-Language-Action Model with Multimodal Chain-of-Thought [66.78110237549087]
  Vision-Language-Action (VLA) models are emerging as a next-generation paradigm for robotics.<n>We introduce dVLA, a diffusion-based VLA that unifies visual perception, language reasoning, and robotic control in a single system.
  arXiv  Detail & Related papers  (2025-09-30T02:36:11Z)
• NinA: Normalizing Flows in Action. Training VLA Models with Normalizing Flows [75.70583906344815]
  Diffusion models have been widely adopted as action decoders due to their ability to model complex, multimodal action distributions.<n>We present NinA, a fast and expressive alternative to diffusion-based decoders for Vision-Language-Action (VLA) models.
  arXiv  Detail & Related papers  (2025-08-23T00:02:15Z)
• FastCar: Cache Attentive Replay for Fast Auto-Regressive Video Generation on the Edge [60.000984252907195]
  Auto-regressive (AR) models have recently shown promise in visual generation tasks due to their superior sampling efficiency.<n>Video generation requires a substantially larger number of tokens to produce coherent temporal frames, resulting in significant overhead during the decoding phase.<n>We propose the textbfFastCar framework to accelerate the decode phase for the AR video generation by exploring the temporal redundancy.
  arXiv  Detail & Related papers  (2025-05-17T05:00:39Z)
• VLA-Cache: Efficient Vision-Language-Action Manipulation via Adaptive Token Caching [23.52474883720957]
  Vision-Language-Action (VLA) models have demonstrated strong multi-modal reasoning capabilities, enabling direct action generation from visual perception and language instructions.<n>This paper introduces VLA-Cache, a training-free inference acceleration method that reduces computational overhead by adaptively caching and reusing static visual tokens across frames.
  arXiv  Detail & Related papers  (2025-02-04T09:48:14Z)
• Dynamic Frame Interpolation in Wavelet Domain [57.25341639095404]
  Video frame is an important low-level computation vision task, which can increase frame rate for more fluent visual experience. Existing methods have achieved great success by employing advanced motion models and synthesis networks. WaveletVFI can reduce computation up to 40% while maintaining similar accuracy, making it perform more efficiently against other state-of-the-arts.
  arXiv  Detail & Related papers  (2023-09-07T06:41:15Z)
• ReBotNet: Fast Real-time Video Enhancement [59.08038313427057]
  Most restoration networks are slow, have high computational bottleneck, and can't be used for real-time video enhancement. In this work, we design an efficient and fast framework to perform real-time enhancement for practical use-cases like live video calls and video streams. To evaluate our method, we emulate two new datasets that real-world video call and streaming scenarios, and show extensive results on multiple datasets where ReBotNet outperforms existing approaches with lower computations, reduced memory requirements, and faster inference time.
  arXiv  Detail & Related papers  (2023-03-23T17:58:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.